Persons with recent hand amputations expect modern hand prostheses to function like intact hands. Current state-of-the-art electric prosthetic hands are generally single degree-of-freedom (opening and closing) devices that are controlled using only two muscle signals. As a result, most state-of-the-art devices fail to meet user's expectations and are under-utilized or rejected. Because of this, advances in mechanical hardware are directed toward providing functionality comparable to the intact human hand. Despite such advances, the performance of sophisticated hand prostheses remains limited by the ability to control them via physiological (e.g., electromyographic) signals sensed from the user. In general, prosthetic devices that support multiple degree-of-freedom movements for any limb require sequential control, implementing locking mechanisms or special switch signals to change from one degree-of-freedom to another. There is a large, unmet need for control algorithms that allow simultaneous control of multiple degrees-of-freedom and are not difficult for the user to learn. In this study, we will implement a biomechanical modeling approach to develop a control algorithm that predicts the hand and wrist motions that would occur in an intact hand given the electromyographic (EMG) signals measured from the residual muscles of an amputee's forearm. The objectives for this proposal are to first characterize the function of the hand muscles in creating complex hand motions in the intact hand and to then develop the controller. To accomplish these objectives, extrinsic muscle activity and joint kinematics will be quantified as individuals produce a subset of postures from the manual alphabet of American Sign Language (ASL), and perform two prehensile tasks. Recorded muscle activity will define the control signals available from the extrinsic muscles during complex motions, and will become input for biomechanical simulations, which will be used to identify how effectively postures can be achieved without the contributions from the intrinsic muscles of the hand (the subset of muscles lost to amputation). Results will direct the mechanical design of prosthetic hands to effectively compensate for the mechanical actions of the missing intrinsic muscles. Ultimately, a prosthetic hand is intended to be used to manipulate objects. Thus, we will implement recent developments in variational integration theory to develop real-time simulations that incorporate endpoint forces, such as those found when the fingertips are in contact with an object, and other constraints required to simulate the hand interacting with external objects. Upon completion of the simulation work, a controller that drives the artificial hand based on user-generated muscle signals will be developed and implemented. Accomplishing the goals of this project will address a critical barrier to clinical implementation and user acceptance of multi-function prosthetic hands.